GLM-5.3-Flash-Maya-GGUF

模型介绍

**Maya-L: 99.2% of the full FP8 model's accuracy** on zero-shot tasks (ARC, HellaSwag, WinoGrande, PIQA), in 156 GB. **Maya-S: 97.9%** in a 96 GB file. **Maya-S24: 97.7%, and up to 14% faster decode on 24 GB cards**, in 94.7 GB. **Maya-M: 97.9%, and closer to the full model token by token**, in 116 GB. GGUF quantizations of zai-org/GLM-5.3-Flash (321 B parameters, a mixture of experts with about 18 B active per token), made for **Project Maya**. Project Maya keeps the most-used experts on the GPU, the next in RAM and the rest on the SSD, so all four run well below the model's size (two GPUs with 30 GB of RAM in the measurements below). More memory is faster, and every machine is different: try it on yours. All four are made from Z.ai's own FP8 release - the precision the model is served at - not from a re-quantized file. - **Maya-S** (96.5 GB) is the compact one, made for PCs with a smaller memory pool across RAM and VRAM: its routed experts - most of the model - in about 2 bits (IQ2_XXS / IQ2_S), its attention and shared experts in 6-bit (Q6_K). - **Maya-S24** (94.7 GB) is Maya-S for 24 GB cards (RTX 3090 / 4090): the same 2-bit routed experts, with only the small part every token runs through - the attention and shared experts - in 4-bit (Q4_K) instead of Maya-S's 6-bit. That leaves about 1.5 GB more room for experts on the GPU. - **Maya-M** (116 GB) is closer to the full model, for PCs with a bigger memory pool: built with the same FP8-statistics recipe and more bits where they count (2- to 3-bit experts), calibrated toward tool calls and front-end code. - **Maya-L** (156.3 GB) is the closest, for PCs with the biggest memory pool: Maya-M's recipe one step up (3- to 4-bit experts: IQ3_S / IQ4_XS, Q5_K in the most sensitive layers).

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。